Fruit quality prediction based on soil mineral element content in peach orchard

Abstract Mineral nutrition of orchard soil is critical for the growth of fruit trees and improvement of fruit quality. In the present study, the effects of soil mineral nutrients on peach fruit quality were studied by using artificial neural network model. The results showed that the four established ANN models had the highest prediction accuracy (R 2 = .9735, .9607, .9036, and .9440, respectively). The results of prediction model sensitivity analysis showed that available B, Ca, N, and K in the soil had the greatest influence on the single fruit weight, available Fe, K, B, and Ca in the soil had the greatest effect on fruit soluble solid content, available Ca, N, B, and K in the soil had the greatest influence on the fruit titratable acid content, and available Ca, Fe, N, and Mn in the soil had the greatest effect on fruit edible rate. The response surface methodology analysis determined the optimal range of these mineral elements, which is critical for guiding precision fertilization in peach orchards and improving peach fruit quality.


| INTRODUC TI ON
Peach (Prunus Persica (L.) Batsch) was one of the most valuable stone fruit crops in the Rosaceae family , native to the northwest of China. It is currently the third-largest deciduous fruit tree in China (Yu et al., 2019), second only to apples and pears. It has been cultivated in over 80 countries worldwide for its economic, social, and ecological benefits (Tian, 2020). Peach is a fruit with high nutritional value, rich in mineral elements, protein, sugar, fat, vitamins, and other nutrients, and is deeply loved by people (Serra et al., 2020;Wang, Liu, et al., 2021;Yu et al., 2010).
Fruit quality was the central goal of fruit cultivation technology.
Fruit's competitiveness in the market can only be improved by good fruit quality. In recent years, with the continuous improvement of people's living standards, they also raised their expectation for the nutritional value and fruit quality . Several studies found that the soil mineral element content in orchard had a significant impact on fruit quality. The multivariate analysis of soil nutrients and fruit quality of kiwi orchard revealed that the soluble sugar of kiwi fruit was mainly affected by the available potassium and available sulfur, and the titratable acid was mainly affected by the organic matter (Chen et al., 2021). The nitrogen content of orchard soil directly affects the fruit quality and yield of peach (Zhu et al., 2019), there was a certain correlation between soil organic content, available potassium content, and peach fruit weight Wang, Liu, et al., 2021). The correlation analysis between soil mineral nutrients and fruit quality of Jinsha pomelo showed a negative correlation between total sugar and available iron in the soil, and a negative correlation between edible rate and available manganese in soil (Yu et al., 2021). Currently, the research on fruit quality and mineral nutrients at home and abroad was only a simple difference and correlation analysis, which are incapable of revealing its complex internal relationship.
Artificial neural network (ANN) was a mathematical model, based on the basic principle of neural network in biology. After comprehending the structure of human brain and the response mechanism of external stimuli, it is possible to simulate the processing mechanism of complex information of the human brain nervous system using knowledge of network topology (Saffari et al., 2009). A complex network system was formed by many simple processing units (called neurons) connected with each other. As the basis of deep learning, neural network model played an important role. The neural network was an extensive parallel network composed of adaptive simple units, which can simulate the interaction between biological neural system and real-world objects. It mainly consisted of three layers: input, hidden, and output (Tracey et al., 2011). It can make the machine recognize the pattern and trend of data through a special algorithm, and successfully predict and classify.
Because of its good fault tolerance and good self-learning ability, it had attracted the attention of scholars in many fields. In recent years, the ANN was more and more widely applied in the field of agriculture. Response surface methodology and artificial neural network could optimize the extraction of polysaccharides and polyphenols from blackcurrant fruit (Bu et al., 2021). The ANN model was used to predict and optimize the main quality parameters of corn for ethanol production (Voca et al., 2021). The ANN model has been shown to be an effective and reliable forecasting tool in many studies (Azarmdel et al., 2020;Banga et al., 2020;Huang et al., 2021;Kumar et al., 2020).
The present study used various ANN models to study the effects of soil mineral nutrient content on peach fruit quality, and the suitable range of main mineral elements was identified, providing a theoretical basis for precise fertilization of peach orchards.

| Materials
The experiment was carried out in Wuxi, Yancheng, and Changzhou in Jiangsu Province, which were also the main peach planting areas of Jiangsu. We chose 75 peach orchards with basically the same cultivation and management level. The fertilization method was mainly to apply organic fertilizer in autumn, about 1.5-1.8 tons per mu (666.67 m 2 ). Nitrogen, phosphorus, and potassium fertilizer were applied before the fruit expansion stage, about 15 kg, 8 kg, and 20 kg, respectively. The main cultivar of peach in orchard was "Hujingmilu", six healthy adults with the same growth status and medium crown size were randomly selected as sample plants in each orchard. Twenty to thirty fresh fruits with normal maturity and similar size were randomly collected from each orchard. The sampling orientation and canopy were all the same. Four points were determined in the east, west, south, and north directions under the crown drip line of each sampling tree, and the surface soil of 0-30 cm was drilled with a soil sampler. After removing the sundries, the obtained soil was mixed evenly. A sample of approximately 1 kg of soil was quartered, dried naturally in the laboratory, ground into powder, and stored in a marked sealed bag after passing through a 100-mesh nylon sieve (Safa et al., 2018).

| Experimental methods
The content of soil available N was extracted by the ion exchange resin bag method , and then determined by AA3 continuous flow analyzer (Wang, 2020). The contents of available P, K, Ca, Mg, Fe, Mn, Cu, Zn, and B in soil were extracted by AB-DTPA extraction method (Hao et al., 2016) and determined by Agilent 710 ICP-OES inductively coupled plasma atomic emission spectrometry (Li et al., 2018;Huang et al., 2018).
We used the 1/10,000 electronic analytical balance to determine the single fruit weight of peach, the pal-1 portable digital display sugar meter to determine the soluble solid content of peach, and the titratable acid content of peach was quantitatively measured via acid-base titration (Cao et al., 2007). The edible rate = (single fruit weight-single fruit stone weight)/single fruit weight *100.

| Statistical analysis and neural models building
The artificial neural network model was constructed using soil mineral element content as input layer and peach fruit quality index as output layer. During the model development process, we randomly use 70% of the data for model training, 15% of the data for model validation, and the remaining 15% of the data for model testing.
Meanwhile, we preprocessed the original data using the following formula (Shabani et al., 2017): Where M is the original measurement value, M n is the prepro- •Log-sigmoid function: •Tangent-sigmoid function:

Linear function:
Where n is the number of data, T i is the original measured values, t i is the predicted values of the established model, and the bar is the average value of the concerned variable. We constructed the best prediction model using neural network, and then eliminated model independent variables one by one to perform model sensitivity analysis. This allowed us to investigate the mineral elements that have a significant influence on fruit quality indexes. Finally, response surface analysis is used to determine the appropriate range of these mineral elements for the best fruit quality by analyzing the content of major mineral elements and the corresponding fruit quality indexes.

| Fruit quality and soil mineral element content of peach orchards
The peach quality indicators of different orchards are shown in Table 1, the maximum value of the single fruit weight was 393.30 g, the minimum was 162.20 g, and the average value was 281.99 g.
The maximum soluble solid content was 17.96%, the minimum was 9.60%, and the average value was 13.27%. The maximum value of the titratable acid content was 0.51%, the minimum was 0.14%, and the average value was 0.32%. The maximum value of the edible rate was 96.25%, the minimum was 92.70%, with an average value of 94.53%. Among them, the variation coefficient of titratable acid content was the largest (26.14%), and that of edible rate was the smallest (0.96%), indicating that the difference of titratable acid content in different orchards was significant; however, the difference of edible rate was minor.
The contents of soil mineral elements in different orchards are shown in Table 2. The available macro-element average values of N, P, K, Ca, and Mg were 197.48 mg/kg, 71.33 mg/kg, 440.80 mg/ kg, 220.96 mg/kg, and 154.20 mg/kg, respectively. The coefficient of variation of P and Mg was the largest, which indicated that there were great differences between the two macro-elements in different orchards. The micro-element average values of available Fe, Mn, Cu, Zn, and B were 168.07 mg/kg, 79.57 mg/kg, 9.15 mg/kg, 7.67 mg/ kg, and 0.64 mg/kg, respectively. The coefficient of variation of Cu and Zn was the largest, which indicated that there were significant differences between the two micro-elements in different orchards.

| ANNs model for predicting the single fruit weight
To further explore the relationship between the content of mineral elements in soil and fruit quality, we established a model using ANNs and predicted fruit quality through the content of mineral elements in soil. To develop a reliable prediction model of single fruit weight, we used five different training functions and three different transfer functions to evaluate the prediction performance of the ANN models (Table 3). Meanwhile, we also tested the structure

| ANNs model for predicting the soluble solid content
Similarly, we established a prediction model to accurately predict the soluble solid content of fruit based on the content of soil mineral elements ( Table 4)

| ANNs model for predicting the titratable acid content
Similarly, using the content of soil mineral elements, we established a prediction model to accurately predict the fruit titratable acid content (Table 5). When the Log-sigmoid transfer function was used, the prediction accuracy of LM transfer function was the highest

| ANN model for predicting the fruit edible rate
Similarly, we established a prediction model to accurately predict the fruit edible rate by the content of soil mineral elements (Table 6).
When using the Log-sigmoid transfer function, the LM transfer function had the highest prediction accuracy (0.9440), which was significantly higher than that of the other four training functions.
When the Liner transfer function was used, the five training func-

| The sensitivity analysis of the soil mineral elements on the peach fruit quality
We obtained reliable prediction models by constructing the ANN

| Response surface methodology analysis
According to the above ANN models' sensitivity analysis results, the content of some mineral elements in the soil had significant effect on fruit quality. To further explore the suitable range of these elements, we carried out a response surface analysis. The response surface analysis of soil available B, Ca content, and single fruit weight is shown in Figure 3a. When soil available B content was 0. 490.0-585.0 mg/kg, a higher single fruit weight can be obtained.
However, when available K content in the soil was greater than 910.0 mg/kg, the single fruit weight index decreased significantly.
The response surface analysis of available Fe, K content in the soil, and soluble solid content is shown in Figure 3c. When soil available When available N content in the soil was 220.0-250.0 mg/kg, available Mn content was 37.0-72.0 mg/kg, higher edible rate can also be obtained.
In conclusion, peach fruit quality indexes can be significantly improved when the content of available N was 71-108 mg/kg, avail-

| The ANN models building and interpretation
The relationship between fruit quality indexes and orchard soil mineral elements was complex and cannot be accurately revealed using  and adaptive ability. The neural network had a certain fault-tolerant ability, which will not affect the global training results when some of its neurons were destroyed (Alvarez et al., 2009;Kumar et al., 2009).
In this study, we built the ANN models by using different training functions and transfer functions, and constantly tested the hidden layer structure. Finally, we obtained four reliable prediction models which can accurately predict fruit quality index of peach. Among them, the topological structure of single fruit weight prediction model was 10-11-1, that of soluble solid content was 10-11-1, that of titratable acid content was 10-11-1, and that of edible rate was 10-9-1, with the highest R 2 value of .9735, .9607, .9036, and .9440, other errors were also the lowest. We also found that the best prediction models used Levenberg-Marquardt training function and Log-Sigmoid transfer function. Many researchers used sigmoid transfer function to predict the relevant indicators of different crops. Belouz et al. (2022) showed that An ANN model with a 12-34-1 topology could more accurately predict tomato yield. Ray et al. (2020) showed that the ANN model with 18-5-1 structure is the best model for predicting the coronarin D content. In addition, we also compared the measured values with the predicted values of the ANN models by scatter plot and box plot, the distribution patterns of the two were almost the same, which further verified the reliability and accuracy of the constructed models.

| The importance of soil mineral nutrients to fruit quality
Fruit quality was one of the market's most important core competitiveness factor, which not only affected fruit price, but also fruit sales volume (Cun et al., 2020). It was caused by a combination of multiple factors, especially the individual and combined effects of mineral nutrients (Aular et al., 2013). The soil was a vital component in the ecosystem's exchange of matter and energy. The abundance and deficiency of soil nutrients had a significant effect on fruit tree growth and development as well as fruit yield and quality (Gao, 2015).
Abundant soil mineral nutrients can promote the healthy growth of fruit trees and play a crucial role in fruit quality (Jin et al., 2010).
In the present study, soil available B content had the greatest effect on fruit weight. B could promote carbohydrate transformation and translocation and accelerate plant growth and development. When B was in abundant supply, the plant was thriving, the root system was good, and the harvest was assured. Otherwise, it can lead to poor plant growth, reduced product quality and yield. Moreover, it was also a participant in sugar transport and metabolism, which had impacted on fruit quality (Fan et al., 2016;Wu, 2020). Additionally, the amount of available Ca content also has a significant impact on single fruit weight. Ca was an important component of plant cell wall, which can promote the division of epidermal cells, improved the toughness and thickness of fruit epidermis, thus accelerating the growth of fruit epidermis and promoting fruit development (Zocchi, 1995). As a result, the content of available Ca was also the most important factor affecting fruit edible rate. When Ca was deficient, it can inhibit the ability of the root to absorb nutrients, lead to plant growth decline, prone to premature senescence, and affect the photosynthesis of plants (Xin, 2008). The results of response surface analysis also showed that when the Ca content was lower than 168 mg/kg, the titratable acid content of fruit would increase. Soil available Fe and K content had the greatest effect on soluble solid content.
For plants, Fe was one of the elements of chlorophyll, which participates in photosynthesis and produces organic matter such as carbohydrates (Jia, 2019). Fe fertilizer treatment can promote the accumulation of soluble sugar and soluble solid content in fruit, which was conducive to the improvement of fruit quality (Guo et al., 2017). K was the activator of more than 60 enzymes, such as synthetase, dehydrogenase, and transporter. It participated in the synthesis and transportation of protein, starch, sugar, and other substances (Pettigrew, 2008 (Wu, 2016). As for the relative contribution, available N, K, Ca, Fe, and B contents in the soil greatly influence fruit quality indexes of peach.

| CON CLUS ION
The ANN methods were used in this study to establish prediction models to explore the effect of soil mineral element content on peach fruit quality. The results indicated that when the prediction model structure of the single fruit weight, the soluble solid content, and the titratable acid content was 10-11-1, and the edible rate prediction model was 10-9-1, which can achieve the highest accuracy (R 2 = .9735, .9607, .9036, and .9440, respectively). The sensitivity analysis results showed that soil available N, K, Ca, Fe, and B content contributed the most to the quality of peach fruit. The response surface methodology analysis confirmed the suitable range of these mineral elements, when the content of available N was 71-108 mg/kg, available K was 490.0-585.0 mg/kg, available Ca was 170.0-198.0 mg/kg, available Fe content was 125-140 mg/kg, and available B content was 0.80-1.02 mg/ kg in the soil, peach fruit quality indexes can be significantly improved.

CO N FLI C T S O F I NTE R E S T
The authors declare that they have no conflict of interest.

E TH I C S A PPROVA L
This article does not contain any studies with animal or human subject.

DATA AVA I L A B I L I T Y S TAT E M E N T
The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding authors.